Introduction

airbnb

Exploring Washington, D.C., is a delight for many travelers, drawn by its rich history, dynamic cultural offerings, and the pulse of political life. For visitors, the choice of where to stay can significantly shape their experience. Airbnb provides a unique option beyond traditional hotels, offering stays that are often more personalized and embedded in local neighborhoods.

This analysis dives into what Airbnb users look for when booking their stays in Washington, D.C. We’ll examine factors like location, amenities, pricing, and host ratings to discover trends and insights that can help both hosts and guests make better-informed choices. Our goal is to shed light on the preferences of Airbnb guests and see how these vary across different areas of the city.

In the upcoming sections, we’ll outline our data sources and the visual tools we’ve used to unravel these patterns. This thorough exploration will equip stakeholders—ranging from hosts and guests to urban planners—with deeper insights into the dynamics of Airbnb accommodations in the capital.

Data Source

Code

import pandas as pd
test = pd.read_csv("listings 2.csv")
test.head(5)

	id	listing_url	scrape_id	last_scraped	source	name	description	neighborhood_overview	picture_url	host_id	...	review_scores_communication	review_scores_location	review_scores_value	license	instant_bookable	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	calculated_host_listings_count_shared_rooms	reviews_per_month
0	3686	https://www.airbnb.com/rooms/3686	20231218032619	2023-12-18	city scrape	Home in Washington · ★4.64 · 1 bedroom · 1 bed...	NaN	We love that our neighborhood is up and coming...	https://a0.muscache.com/pictures/61e02c7e-3d66...	4645	...	4.84	3.91	4.64	NaN	f	1	0	1	0	0.53
1	3943	https://www.airbnb.com/rooms/3943	20231218032619	2023-12-18	city scrape	Townhouse in Washington · ★4.83 · 1 bedroom · ...	NaN	This rowhouse is centrally located in the hear...	https://a0.muscache.com/pictures/airflow/Hosti...	5059	...	4.91	4.57	4.75	Hosted License: 5007242201001033	f	5	0	5	0	2.78
2	4197	https://www.airbnb.com/rooms/4197	20231218032619	2023-12-18	city scrape	Home in Washington · ★4.85 · 1 bedroom · 1 bed...	NaN	Our area, the Eastern Market neighborhood of C...	https://a0.muscache.com/pictures/miso/Hosting-...	5061	...	4.98	4.96	4.95	Hosted License: 5007242201000749	f	2	0	2	0	0.33
3	4529	https://www.airbnb.com/rooms/4529	20231218032619	2023-12-18	city scrape	Home in Washington · ★4.66 · 1 bedroom · 1 bed...	NaN	Very quiet neighborhood and it is easy accessi...	https://a0.muscache.com/pictures/86072003/6709...	5803	...	4.93	4.51	4.83	Exempt	f	2	0	2	0	0.58
4	4967	https://www.airbnb.com/rooms/4967	20231218032619	2023-12-18	previous scrape	Home in Washington · ★4.74 · 1 bedroom · 1 bed...	NaN	NaN	https://a0.muscache.com/pictures/2439810/bb320...	7086	...	4.93	4.21	4.64	NaN	f	3	0	3	0	0.19

5 rows × 75 columns

Our study is based on data from Inside Airbnb, a reputable source that provides comprehensive datasets about Airbnb listings across various cities. Specifically, we obtained detailed listing information from their Washington, D.C. dataset. This dataset includes a wealth of information on thousands of Airbnb properties in the area, covering aspects such as geographical location, pricing, amenities provided, host ratings, and much more.

This rich dataset allows us to perform a nuanced analysis of guest preferences and behavior patterns, providing a granular view of the factors that influence accommodation choices in Washington, D.C. By leveraging this data, we aim to generate actionable insights that can enhance the hosting experience and optimize guests’ stays in the city.

Heat Map Analysis: Median Price and Average Rating

To further understand the landscape of Airbnb accommodations in Washington, D.C., we’ve created heat maps that visually represent the median price and average ratings across different neighborhoods. These maps offer a clear, intuitive display of how prices and guest satisfaction vary geographically throughout the city.

Code

import warnings

# To suppress all warnings
warnings.filterwarnings('ignore')

import geopandas as gpd
import plotly.express as px
import json
import matplotlib.pyplot as plt



dc_bound = gpd.read_file("neighbourhoods.geojson")
df = pd.read_csv("cleaned_data.csv")

# get average rating
specified_review_score_columns = [
    'review_scores_rating', 'review_scores_accuracy', 'review_scores_cleanliness',
    'review_scores_checkin', 'review_scores_communication', 'review_scores_location',
    'review_scores_value'
]

# Calculate the average review score across the specified columns
df['average_review_score'] = df[specified_review_score_columns].mean(axis=1)

# create a new data frame
neighbourhood_data = df.groupby('neighbourhood_cleansed').agg({
    'price_num': 'median',
    'average_review_score':'mean'
}).reset_index()
neighbourhood_data['neighbourhood_cleansed'] = neighbourhood_data['neighbourhood_cleansed'].str.split(',').str[0]

dc_bound['neighbourhood_cleansed'] = dc_bound['neighbourhood']

dc_bound['neighbourhood_cleansed'] = dc_bound['neighbourhood_cleansed'].str.split(',').str[0]
merged_gdf = dc_bound.merge(neighbourhood_data, on='neighbourhood_cleansed', how='right')
merged_gdf['neighbourhood_cleansed'] = merged_gdf['neighbourhood_cleansed'].str.split(',').str[0]
geojson_dict = json.loads(dc_bound.to_json())

Heat Map for median Airbnb Price and average Review by Neighborhood in DC

Code

import plotly.graph_objects as go

# Assuming 'merged_gdf' and 'geojson_dict' are already defined as shown in previous steps.

# Create base figure with map settings
fig = go.Figure(go.Choroplethmapbox(
    geojson=geojson_dict,
    locations=merged_gdf['neighbourhood_cleansed'],  
    featureidkey='properties.neighbourhood_cleansed',  
    z=merged_gdf['average_review_score'],  # initial z values, can be changed by dropdown
    colorscale="tealrose",
    marker_opacity=0.5,
    marker_line_width=0,
    hoverinfo='all'
))


fig.update_layout(
    mapbox_style="carto-positron",
    mapbox_zoom=10,
    mapbox_center={"lat": 38.9, "lon": -77.03},
    margin={"r":0,"t":0,"l":0,"b":0},
    title='Average Airbnb Metrics by Neighborhood in DC'
)

fig.update_layout(
    hoverlabel=dict(
        font=dict(
            family="Courier New, monospace",
            size=12
        ),
        bordercolor='pink',
        bgcolor='white'
    ),
    clickmode='event+select'
)

#  dropdown buttons
fig.update_layout(
    updatemenus=[
        dict(
            buttons=[
                dict(label="Average Review Score",
                     method="update",
                     args=[{"z": [merged_gdf['average_review_score']]},
                           {"title": "Average Airbnb Review Score by Neighborhood in DC"}]),
                dict(label="Median Price",
                     method="update",
                     args=[{"z": [merged_gdf['price_num']]},
                           {"title": "Median Airbnb Price by Neighborhood in DC"}]),
            ],
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.9,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

fig.update_traces(
    hovertemplate=(
        "<b>%{location}</b><br>" +
        "<span style='font-size:0.9em;'>Value:</span> " +
        "<span style='font-size:0.9em;'><b>%{z:.2f}</b></span><br>"
    )
)

fig.show()

Conclusion

In this heatmap visualization of Airbnb listings across Washington D.C., it is observed that the median prices are generally moderate, indicating that most parts of the city offer reasonably priced accommodations. This trend suggests that staying in D.C. can be accessible for a variety of budget levels. Moreover, the average review scores appear to be higher in the northern regions of the city compared to the southern parts. This could indicate a higher satisfaction level or possibly different standards in guest expectations or property offerings in these areas.

This conclusion provides a quick summary and interpretation of the spatial distribution and variations in price and review scores across different neighborhoods in Washington D.C., based on the data visualized in the heatmap.

Optimal Airbnb Booking Locations Based on Custom Preferences

In this analysis, we calculate a composite score for each neighborhood in Washington D.C. by combining the median price and average rating of Airbnb listings. This score is tailored according to user-defined weights, allowing for personalized decision-making based on individual preferences for cost versus quality. The lower the score, the more favorable the neighborhood is for booking, according to the specified preferences.

Code

from sklearn.preprocessing import MinMaxScaler

# Prepare the data
price = merged_gdf['price_num'].values.reshape(-1, 1)  # Reshaping for scaler
score = merged_gdf['average_review_score'].values.reshape(-1, 1)

scaler = MinMaxScaler()

# Normalize the data
merged_gdf['normalized_price'] = scaler.fit_transform(price)
merged_gdf['normalized_score'] = scaler.fit_transform(score)

# Update score calculation to use normalized values
merged_gdf['score'] = (merged_gdf['normalized_price'] * 0.5 + merged_gdf['normalized_score'] * 0.5)

# Create base figure with map settings
fig = go.Figure(go.Choroplethmapbox(
    geojson=geojson_dict,
    locations=merged_gdf['neighbourhood_cleansed'],
    featureidkey='properties.neighbourhood_cleansed',
    z=merged_gdf['score'],  
    colorscale="viridis",
    marker_opacity=0.5,
    marker_line_width=0,
    hoverinfo='all'
))

fig.update_layout(
    mapbox_style="carto-positron",
    mapbox_zoom=10,
    mapbox_center={"lat": 38.9, "lon": -77.03},
    margin={"r":0,"t":0,"l":0,"b":0},
    title='Dynamic Score by Neighborhood in DC based on Weighted Ratios of Price and Review'
)

fig.update_layout(
    hoverlabel=dict(
        font=dict(
            family="Courier New, monospace",
            size=12
        ),
        bordercolor='pink',
        bgcolor='white'
    ),
    clickmode='event+select'
)

# Slider for weight adjustments
sliders = [
    dict(
        active=50,
        currentvalue={"prefix": "Weight of Price "},
        pad={"t": 50},
        steps=[
            dict(method='restyle',
                 args=[
                     {'z': [(merged_gdf['normalized_price'] * k * 0.01) + (merged_gdf['normalized_score'] * (1 - k * 0.01)) if merged_gdf['normalized_score'].iloc[i] * (1 - k * 0.01) != 0 else 0 for i in range(len(merged_gdf))]}
                 ],
                 label=f"{k * 0.01:.2f}") for k in range(100)
        ]
    )
]

fig.update_layout(sliders=sliders)

fig.update_traces(
    hovertemplate=(
        "<b>%{location}</b><br>" +
        "<span style='font-size:0.9em;'>Score:</span> " +
        "<span style='font-size:0.9em;'><b>%{z:.2f}</b></span><br>"
    )
)

fig.show()

Conclusion

Based on the user-defined criteria, neighborhoods with the lowest scores are recommended for Airbnb bookings. This personalized approach helps in making informed decisions, balancing between cost efficiency and guest satisfaction. Users can fine-tune their preferences to find areas that best meet their needs, whether they are looking for the most affordable options or the highest-rated properties.

Addition(Average Rating for each host in each area)

Code

import plotly.graph_objects as go
import pandas as pd
df['neighbourhood_cleansed'] = df['neighbourhood_cleansed'].str.split(',').str[0]

def create_figure():
    neighborhoods = df['neighbourhood_cleansed'].unique()
    
    fig = go.Figure()
    
    for neighbourhood in neighborhoods:
        data = df[df['neighbourhood_cleansed'] == neighbourhood]
        average_rating_by_host = data.groupby('host_name')['average_review_score'].mean().sort_values(ascending=False).head(20)
        
        fig.add_trace(
            go.Bar(
                x=average_rating_by_host.values,
                y=average_rating_by_host.index,
                orientation='h',
                name=neighbourhood,
                visible=(neighbourhood == neighborhoods[0]) 
            )
        )
    
    dropdown_buttons = [
        {
            'label': neighbourhood,
            'method': 'update',
            'args': [
                {'visible': [neighbourhood == n for n in neighborhoods]},
                {'title': f'Review Score by Host in {neighbourhood}'}
            ]
        } for neighbourhood in neighborhoods
    ]
    
    fig.update_layout(
        updatemenus=[{
            'buttons': dropdown_buttons,
            'direction': 'down',
            'showactive': True,
            'x': 0.9,
            'xanchor': 'center',
            'y': 1.5,
            'yanchor': 'top'
        }],
        title=f'Review Score by Host in {neighborhoods[0]}',
        xaxis_title='Average Review Score',
        yaxis_title='Host',
        template='plotly_white' ,
        height=700 
    )
    
    return fig

fig = create_figure()
fig.show()

Code

import ipywidgets as widgets
import seaborn as sns
# Top 20

#def plot_neighbourhood(neighbourhood):
#    data = df[df['neighbourhood_cleansed'] == neighbourhood]
 #   average_rating_by_host = data.groupby('host_name')['average_review_score'].mean().sort_values(ascending=False).head(20)
    
 #   plt.figure()
 #   sns.barplot(x=average_rating_by_host.values, #y=average_rating_by_host.index, palette='viridis')
 #   plt.title(f'Review Score by Host in {neighbourhood}',fontsize = 18)
 #   plt.xlabel('Average Review Score',fontsize = 14)
 #   plt.ylabel('Host',fontsize = 14)
 #   plt.show()

# Dropdown menu for selecting the neighbourhood
#neighbourhoods = df['neighbourhood_cleansed'].unique()

#widgets.interact(plot_neighbourhood, neighbourhood=widgets.Dropdown(options=neighbourhoods, description="Area:")
#                 ,layout={'width': '50%'},style={'description_width': 'initial'},   )

What factors Influencing Airbnb Bookings in Washington D.C. Over the Last 60 Days

Code

# df with only the top ten


neighbourhood_taken_60_sum = df.groupby('neighbourhood_cleansed')['taken_60'].sum().sort_values(ascending=False)

top_ten_neighbourhoods = neighbourhood_taken_60_sum.head(10)
names_only = top_ten_neighbourhoods.index
top_neighbourhoods = list(names_only)
df_withtop_10 = df[df['neighbourhood_cleansed'].isin(top_neighbourhoods)]


# Grouping data by 'neighbourhood_cleansed' and calculating mean for price, latitude, and longitude

data_for_pop = df_withtop_10.groupby('neighbourhood_cleansed').agg({
    'price_num': 'median',
    'latitude': 'mean',
    'longitude': 'mean'
}).reset_index()

dc_wards = gpd.read_file("ACS_Demographic_Characteristics_DC_Ward.geojson")[
    ["NAMELSAD", "DP05_0001E", "geometry"]
]

Top 10 Neighbourhoods by Total Days Booked in 60 Days

Code

import matplotlib.pyplot as plt
plt.figure(figsize=(8, 6))
ax = sns.barplot(x=neighbourhood_taken_60_sum.head(10).index, y=neighbourhood_taken_60_sum.head(10).values, palette="Blues_d")
plt.title('Top 10 Neighbourhoods by Total Days Booked in 60 Days')
plt.xlabel('Neighbourhood')
plt.ylabel('Total Days Booked')
plt.xticks(rotation=45)
plt.grid(True, linestyle='--', alpha=0.6)
sns.despine()

for p in ax.patches:
    ax.annotate(format(p.get_height(), '.1f'), 
                   (p.get_x() + p.get_width() / 2., p.get_height()), 
                   ha = 'center', va = 'center', 
                   xytext = (0, 9), 
                   textcoords = 'offset points')
plt.tight_layout()
plt.show()

This plot provides a visual comparison of the total days booked in the top 10 neighborhoods over the past 60 days. The bar chart ranks neighborhoods according to their booking totals, helping to identify the most popular areas. The visualization also indicates how much more some neighborhoods are preferred over others. This popularity could be influenced by factors such as proximity to tourist attractions, overall safety, or availability of public transport.

Relationship Between Top Booked Neighbourhoods and Population Density

Code

import pandas as pd
import folium
import json
from folium import Icon


dc_wards['Population_Density'] = dc_wards['DP05_0001E'] / dc_wards.geometry.area
m = folium.Map(location=[38.9072, -77.0369], zoom_start=12, tiles='cartodbpositron')  # Changed tiles here

choropleth = folium.Choropleth(
    geo_data=dc_wards,
    data=dc_wards,
    columns=['NAMELSAD', 'Population_Density'],
    key_on='feature.properties.NAMELSAD',
    fill_color='YlGn',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='Population Density in DC'
).add_to(m)

neighbourhood_data = df_withtop_10.groupby('neighbourhood_cleansed').agg({
    'price_num': 'mean',
    'average_review_score':'mean',
    'latitude': 'mean',
    'longitude': 'mean'
}).reset_index()


for index, row in neighbourhood_data.iterrows():
    popup_html = f"""
    <div style="width:200px;">
        <strong>{row['neighbourhood_cleansed']}</strong><br>
        Average price: ${round(row['price_num'], 2)}<br>
        Average rating: {round(row['average_review_score'], 2)}
    </div>
    """
    folium.Marker(
        [row['latitude'], row['longitude']],
        popup=folium.Popup(popup_html, max_width=265),
        tooltip=row['neighbourhood_cleansed'],
        icon=Icon(color='blue', icon='info-sign')
    ).add_to(m)

# Display the map
m

Make this Notebook Trusted to load map: File -> Trust Notebook

This plot leverages a choropleth map to show population density across different wards in DC, with overlays of markers indicating average price and ratings in the top booked neighborhoods. The map reveals any correlation between population density and booking frequency. High population density might suggest more local amenities and better connectivity, which could appeal to Airbnb users.

Relationship Between Top 10 Booked Neighbourhoods and Median Price

Code

import pandas as pd
import plotly.graph_objects as go


fig = px.choropleth_mapbox(
    merged_gdf,
    geojson=geojson_dict,
    locations='neighbourhood_cleansed',  
    featureidkey='properties.neighbourhood_cleansed',  
    color='price_num',  
    color_continuous_scale="tealrose",  
    mapbox_style="carto-positron",  
    zoom=10,  
    center={"lat": 38.9, "lon": -77.03}, 
    opacity=0.5,  
    labels={'neighbourhood_cleansed':'Area','price_num': 'Average Price',}, 
    hover_data={
        'neighbourhood_cleansed': True,
        'price_num': ':.2f',  
    }
)
fig.update_traces(
    hovertemplate=(
        "<b>%{customdata[0]}</b><br>"
        "<span style='font-size:0.9em;'>Average Price:</span> "
        "<span style='font-size:0.9em;'><b>$%{customdata[1]:.2f}</b></span><br>"
    )
)

fig.add_trace(
    go.Scattermapbox(
        lat=neighbourhood_data['latitude'],
        lon=neighbourhood_data['longitude'],
        mode='markers',
        marker=go.scattermapbox.Marker(
            size=9,
            color='black',
            opacity=0.7
        ),
        text=neighbourhood_data.apply(lambda row: f"{row['neighbourhood_cleansed']}<br>Avg. Price: ${round(row['price_num'], 2)}", axis=1),
        hoverinfo='text'
    )
    
)

fig.update_layout(
    hoverlabel=dict(
        font=dict(family="Courier New, monospace", size=12),
        bordercolor='pink',
        bgcolor='white'
    ),
    title='Average Airbnb Price and Review by Neighborhood in DC',
    margin={"r": 0, "t": 0, "l": 0, "b": 0},
    clickmode='event+select'
)

fig.show()

This plot combines a choropleth map and scatter markers to illustrate the median price of Airbnbs in each of the top neighborhoods. This visualization helps to assess whether price plays a significant role in the popularity of certain areas. For instance, neighborhoods that offer a good balance between cost and amenities might see higher booking rates.

Relationship Between Top 10 Booked Neighbourhoods and Average Rating

Code

import pandas as pd
import plotly.graph_objects as go

fig = px.choropleth_mapbox(
    merged_gdf,
    geojson=geojson_dict,
    locations='neighbourhood_cleansed',  
    featureidkey='properties.neighbourhood_cleansed',  
    color='average_review_score',  
    color_continuous_scale="tealrose",  
    mapbox_style="carto-positron",  
    zoom=10,  
    center={"lat": 38.9, "lon": -77.03}, 
    opacity=0.5,  
    labels={'neighbourhood_cleansed':'Area','average_review_score':'Average rating'}, 
    hover_data={
        'neighbourhood_cleansed': True,
        'average_review_score': ':.2f'  
    }
)
fig.update_traces(
    hovertemplate=(
        "<b>%{customdata[0]}</b><br>"
        "<span style='font-size:0.9em;'>Average Rating:</span> "
        "<span style='font-size:0.9em;'><b>%{customdata[1]:.2f}</b></span>"
    )
)

fig.add_trace(
    go.Scattermapbox(
        lat=neighbourhood_data['latitude'],
        lon=neighbourhood_data['longitude'],
        mode='markers',
        marker=go.scattermapbox.Marker(
            size=9,
            color='black',
            opacity=0.7
        ),
        text=neighbourhood_data.apply(lambda row: f"{row['neighbourhood_cleansed']}<br>Avg. Rating: {round(row['average_review_score'], 2)}", axis=1),
        hoverinfo='text'
    )
    
)

fig.update_layout(
    hoverlabel=dict(
        font=dict(family="Courier New, monospace", size=12),
        bordercolor='pink',
        bgcolor='white'
    ),
    title='Average Airbnb Price and Review by Neighborhood in DC',
    margin={"r": 0, "t": 0, "l": 0, "b": 0},
    clickmode='event+select'
)

fig.show()

Similar to the previous plot, this map integrates a choropleth display with marker overlays but focuses on average review scores instead of prices. High review scores can be a strong indicator of customer satisfaction and can significantly influence booking decisions. This plot helps to understand if there’s a strong correlation between the quality of stay (as reflected in reviews) and booking volumes.

Conclusion

After reviewing the data and visualizations concerning Airbnb bookings in Washington D.C. over the last 60 days, it appears that guests prioritize staying in areas with higher population density and more affordable prices. These factors seem to outweigh the importance of average ratings in influencing booking decisions. This trend suggests that visitors value convenience and cost-effectiveness, possibly due to better access to amenities and transport options in denser, more affordable areas, over the perceived quality or reviews of the listings.

What Hosts Want People to Know About Their Listings

In an effort to better understand how hosts describe their offerings on our platform, we’ve analyzed the ‘About’ sections from various listings. By extracting key terms and phrases from these descriptions, we’ve created a visual representation through a word cloud. This visualization highlights the most frequently mentioned features and attributes, giving us a clearer picture of what hosts believe are the most appealing aspects of their properties.

Code

import pandas as pd
import numpy as np
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import string
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from collections import Counter
from nltk.corpus import stopwords
import nltk


host_about_text = df['host_about'].dropna()
combined_text = " ".join(host_about_text)

# Removing punctuation
translator = str.maketrans('', '', string.punctuation)
text_no_punctuation = combined_text.translate(translator)
nltk.download('stopwords')

# Set of English stopwords
stop_words = set(stopwords.words('english'))
# Manually defining a basic set of English stopwords
# basic_stopwords = set([
#    "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours",
#    "yourself", "yourselves", "he", "him", "his", "himself", "she", "her", "hers",
#    "herself", "it", "its", "itself", "they", "them", "their", "theirs", "themselves",
#    "what", "which", "who", "whom", "this", "that", "these", "those", "am", "is", "are",
#    "was", "were", "be", "been", "being", "have", "has", "had", "having", "do", "does",
#    "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until",
#    "while", "of", "at", "by", "for", "with", "about", "against", "between", "into",
#    "through", "during", "before", "after", "above", "below", "to", "from", "up", "down",
#    "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here",
#    "there", "when", "where", "why", "how", "all", "any", "both", "each", "few", "more",
#    "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so",
#    "than", "too", "very", "s", "t", "can", "will", "just", "don", "should", "now","washington","dc","you'll"
#])

# Remove basic stopwords
text_no_basic_stopwords = ' '.join([word for word in text_no_punctuation.lower().split() if word not in stop_words])
mask = np.array(Image.open("comment.png"))
# Generate the word cloud with the simplified stopword set
wordcloud = WordCloud(width = 800, height = 800, 
                background_color ='white', 
                stopwords = stop_words, 
                min_font_size = 10,mask=mask).generate(text_no_basic_stopwords)

# Display the word cloud
plt.figure(figsize = (7, 7), facecolor = None) 
plt.imshow(wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
plt.show()

[nltk_data] Downloading package stopwords to
[nltk_data]     /Users/tongge/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

Conclusion

The word cloud generated from the host descriptions really captures what’s valued most in rental listings, with “Washington DC” being a standout. For sure the hosts want people to know where they are. Also, the word “love” popping up frequently suggests that hosts really put heart into their properties, want to create a warm atmosphere.

Phrases like “fully equipped” and “thoughtfully designed” shine through too, indicating that hosts strive to offer more than just the basics. These terms likely refer to amenities like well stocked kitchens and pleasing decor, crucial for travelers who want a “home away from home.” The mention of “travel” ties directly to the guest’s needs, hinting that hosts think about what conveniences will make travel smoother.